Method and system to determine anaerobic threshold of a person non-invasively from freely performed exercise and to provide feedback on training intensity

ABSTRACT

A method and system for determining anaerobic threshold intensity (AnT) of a user in a freely performed physical exercise. A physiological response of a user is measured by heart rate and measured heart rate values are recorded as heart rate data. An external workload values are recorded and are each associated with one measured heart rate values to form a plurality of data points. The data points are filtered to form accepted data points, which are classified within a plurality of heart rate segments representing a heart rate within an anaerobic threshold (AnT) of the user. A data point with highest probability is stored for each segment. A first probability factor for each accepted data point is calculated. The calculated first probability factor is compared to a stored probability factor in each segment, and the higher probability factor is retained. AnT is calculated using the stored probabilities in each segment.

FIELD

The disclosure may relate to an improved method and system fordetermining a person's anaerobic threshold intensity (AnT) from freelyperformed exercise and to provide feedback.

BACKGROUND

In order to individualize training intensity according to cardiovascularand metabolic stress (exercise stress), rather than absolute externalworkload, different methods for determining exercise intensity have beenused. These methods have been, for example, based on % HRmax, % VO2max,% HRreserve (% HRR) or % VO2reserve (% VO2R). In addition, intensityzones that are based on metabolic thresholds have been used. Lactatethreshold (LT) and Anaerobic threshold (AnT) (or onset of blood lactateaccumulation OBLA) are such metabolic thresholds corresponding to alactate threshold of about 2.5 mmol/l and 4.0 mmol/l, respectively.Training zones have been divided between these thresholds: 1) “Basicendurance training” or “long slow distance” below LT, including allsteady pace exercises in which lactate is below 2.0 or 2.5 mmol/l level;2) “Threshold training” between LT and AnT including steady pace andinterval training with lactate values between 2.5 and 4.0 mmol/1; 3)VO2max training above AnT with lactate values over 4.0 mmol/l. As wouldbe understood by a person of ordinary skill in the art, other intensityzone models have been applied in training. They may be informative intraining practice, but it is important to note that they are not basedon clearly defined physiological markers.

AnT may be metabolically characterized as the highest workload at whichthe body is able to achieve steady-state condition, which means that thelactate (specifically lactic acid) accumulation and removal (bymetabolizing) is in balance so that lactate level stays stable. Forexample, if a person's AnT pace for running is 4:00 min/km, the personis able to run with that speed with a constant lactate level of about 4mmol/l for prolonged periods. If the person increases pace to, forexample 3:50 min/km, he/she may no longer able to achieve a steadystate. Instead the person's lactate level may accumulate from anapproximate 4 mmol/l starting level up to 10 mmol/l or higher untilsubjective fatigue takes place. Further, AnT may not be an exact lactatelevel but may vary between individuals. Lactate level corresponding toAnT may usually be between 3.0 and 4.0 mmol/l and may depend, forexample, on personal physiological characteristics, or other factors aswould be understood by a person of ordinary skill in the art.

Despite small individual variation in lactate levels corresponding toAnT, the same or similar physiological reactions may be related to it.For example, when exercise intensity is increased gradually from rest,at certain points anaerobic energy pathways usually start to noticeablyactivate and support the aerobic energy system in producing energy,which may sustain the energy demands of the body in the form of ATP.When anaerobic energy pathways are activated, glycogen/glucose can beused more rapidly to form ATP through glycolysis. This may result infast lactate formation in the muscles. Until AnT, the lactate can bemetabolized by the body without continuous accumulation. If exerciseintensity is increased above AnT, aerobic energy production capabilitiesof the working muscles may have difficulties in matching the exerciseenergy requirements, and anaerobic energy production may increaserapidly. Consequently, lactic acid (lactate) may start to accumulateinto the muscles and blood stream. When exercise intensity exceeds AnT,accumulation of lactic acid in muscles may cause fatigue in a briefperiod of time.

As would be understood by a person of ordinary skill in the art, similarterms may refer to the same physiological phenomenon as AnT.Non-limiting examples may include onset of blood lactate accumulation(OBLA), maximal lactate steady-state (MLSS), and respiratorycompensation thresholds. All of these may refer practically to the sameexercise intensity where lactic acid starts to accumulate due to thebody's inability to remove lactic acid by oxidation and glucosere-formation (gluconeogenesis). This may cause a reduction in bloodbicarbonate levels because bicarbonate can buffer the rise in acidity.Consequently, the body's carbon dioxide (CO2) production may beincreased, thus possibly leading to increased CO2 removal from the bodyby means of increased respiration rate and ventilation. This rapidincrease in ventilatory parameters can be used to detect thesethresholds when exercise intensity is increased incrementally (forexample, in test situations). Another detectable sign in incrementalexercise tests may be the deflection point of heart rate. That is, atlow to moderate intensities heart rate may increase linearly in relationto external work performed (e.g. speed or watts), but at AnT intensity,the increase in heart rate may start to slow. To clarify, this kind ofmetabolic threshold can be determined for everyone, whether one issedentary, highly trained, or otherwise, but the exact lactate level mayvary depending on individual physiological characteristics, for examplebetween 3-4 mmol/l. Regardless of the exact lactate value, the same orsimilar physiologic responses may occur and these responses are measuredin order to determine the threshold intensity. Heart rate level and/orpace (e.g. min/km or km/h) corresponding to AnT may be relevant trainingparameters since either one or both can be measured during any exerciseand the user can easily observe whether he/she is on the right intensityzone or not. Pace (e.g. min/km or km/h) corresponding to AnT may allowan individual to, for example, track changes in fitness level becauseAnT-pace is a relevant predictor of, for example, marathon performance.Alternatively, other training parameters can be measured as would beunderstood by a person of ordinary skill in the art.

Accordingly, AnT may present an exercise intensity level that isimportant to long-term performances as it may represent the highestintensity of performance that can be tolerated for relatively longperiods. The metabolic characteristics of AnT (and other similar lactatederived threshold values such as OBLA and MLSS) may be related to thoseof critical power, which is a concept that aims to represent the highestworkload at which it is possible to perform, for example, 30 min to 60min all-out time trials. In practice, the intensity may be higher atcritical power than at AnT, and the AnT has been associated with a lowerworkload and increased time to exhaustion when compared to criticalpower.

Downsides of the current methodologies to define AnT are well known. Theavailable methods for estimating the AnT require specific exerciseprotocols with incremental exercise intensity. Moreover, laboratorytests are invasive since blood lactate samples are used to determineAnT. Further, a single incremental test might not be accurate in eachcase, as there is variation in the individuals' performance level fromday to day. Additionally, laboratory tests can cause anticipation and bestressful, which may affect physiology and the accuracy of the results.Therefore, it would be very beneficial if anaerobic threshold could benon-invasively analyzed from day to day with freely performed real-lifeexercises outside of laboratory conditions. This may be easier for theusers, and as more data on determined anaerobic threshold may becomeavailable, it may also increase accuracy and reliability of thedetermined anaerobic threshold value.

SUMMARY

Exemplary embodiments of the disclosure may determine a person'sanaerobic threshold intensity from, for example, freely performedexercise, and may further provide feedback.

In one exemplary embodiment, a method for determining a person'sanaerobic threshold intensity (AnT) in a freely performed physicalexercise may be conducted according to the following exemplary steps:

-   -   a. the user may start to perform an exercise;    -   b. physiological response may be continuously measured by heart        rate, and measured heart rate values may be recorded with time        stamp as heart rate data;    -   c. external workload may be continuously measured, and measured        workload values may be recorded and associated with recorded        heart rate values, and each heart rate value and associate        workload value may form a data point;    -   d. unreliable data points may be filtered using predetermined        criteria, and remaining points may form accepted data points;    -   e. accepted data points may be classified to selected narrow        segments regarding heart rate covering AnT, including segments        below and above a probable heart rate value of AnT, and at least        one data point in each segment may be stored in records;    -   f. a first factor P1 may be calculated for probability for each        accepted data point based on external workload and heart rate        variability, wherein factor P1 may be part of total probability        P[i] which may depict a likelihood of AnT being in respective        segment [i].    -   g. the calculated probability factor P1 may be compared with a        previous value in a respective segment, and if higher, the        respective data point may be updated in the record, and if        otherwise, it may then be disregarded; and    -   h. when requested, continuing to a full calculation, wherein    -   i. executing the full calculation of the estimate of AnT may        include calculating optional remaining (P2, P3) factors for each        total probability P[i], the full calculation using the stored        data of the said records; and    -   j. calculating an estimate of AnT as a weighted value of all        heart rates of each segment and each total probability P[i]        therein.

In another exemplary embodiment the overall probability may be split tofactors (P1, P2 . . . ). The major part of total probability (P1) may bechosen so that the need of calculation may be minimized, but fullcalculation can be executed whenever AnT should be given to the user.Full calculation may use segmented data as a base. In that exemplaryembodiment, candidates may be calculated into segments, and updatingremaining parts (P2, P3) may take place rarely. The segment data mayinclude, for example, elapsed time, heart rate, external workload (e.g.theoretical oxygen consumption, watts or speed (v)), maximum externalworkload during the exercise (e.g. maximum speed (v_(max))), meanaverage HRV (MAD), minimum MAD (MADmin), or other data as would beunderstood by a person of ordinary skill in the art.

In another exemplary embodiment, the major part (P1) of totalprobability (P) may be calculated, for example, based on therelationship between current external workload and highest measuredexternal workload, and the relationship between current and lowestmeasured heart rate variability level.

In another exemplary embodiment, remaining factors (P2,P3) may at leastbe based on a user's fitness level and population reference values, thepopulation reference values being related to the normal location on AnTin HR-scale, as would be understood by a person of ordinary skill in theart. In one example, it may be about 90% of HRmax, but could be anysimilar value without departing from the scope of the disclosure. Insome embodiments, a population reference value may be based, forexample, on linear dependency between heart rate and external workload.

In other exemplary embodiments, intensity level corresponding to a bestestimate of the user's anaerobic threshold is determined. Determinedintensity level corresponding to a user's anaerobic threshold can beexpressed as an absolute or relative value describing intensity, forexample, heart rate (HR) level, percentage of maximal intensity (%HRmax, % VO2max, % METmax), pace (min/km or min/mile), speed (km/h ormph), theoretical VO2 (ml/kg/min) or any other parameter describingexercise intensity.

In another exemplary embodiment, the user may be advised through thefeedback regarding an aim of the exercise, and/or the user may beadvised by the feedback to, for example, choose an exercise type from apreset group of different exercise types.

Anaerobic threshold estimate can be given during and/or after exerciseto the user, or to any external system.

The method could be implemented in any device comprising a processor,memory and software stored therein and a user interface, for example, aheart rate monitor, fitness device, mobile phone, PDA device, wristopcomputer, personal computer, and the like.

The following Table 1 may show exemplary definitions and abbreviationsof terms used in the exemplary embodiments described herein.

TABLE 1 Exemplary Definitions and Abbreviations Term or abbreviationDefinition AnT Anaerobic threshold. Refers to the highest velocity orexternal power output that a person's can maintain during physicalactivity without continuous lactic acid accumulation. HR Heart rate(beats/min) HRmax maximum heart rate (of a person) (beats/min) % HRmaxor phr heart rate relative to maximum heart rate VO2 Oxygen consumption(ml/kg/min) VO2max maximum oxygen consumption capacity of a person(ml/kg/min) % VO2max measured VO2 relative to VO2max of a person % VO2R(measured VO2-resting VO2)/(VO2max- resting VO2) Theoretical VO2 orValue that describes external workload theoretical oxygen (ml/kg/min).consumption Can be calculated based on speed and altitude change (orspeed and grade of inclination), or based on measured power output inbicycles and other exercise equipment. METmax/maxMET/ maximum oxygenuptake capacity of a maximal_MET person relative to resting oxygenconsumption =VO2max (ml/kg/min)/resting VO2 (ml/kg/min) =VO2max(ml/kg/min)/3.5 ml/kg/min v In this application may refer EITHER toactual velocity measured during physical activity OR to a calculatorylevel-ground running velocity that has been converted from measuredpower output (watts) or measured running speed at any grade ofinclination (for example product of measured running speed and measuredaltitude change). v_max or vmax In this application may refer EITHER toactual maximum velocity measured during physical activity OR to acalculatory maximal level-ground running velocity that has beenconverted from measured power output (watts) or measured running speedat any grade of inclination (for example product of measured runningspeed and measured altitude change). R-R-interval Time interval betweensuccessive heart beats HRV Heart rate variability meaning the variationin time interval between successive heart beats. The magnitude of heartrate variability may be calculated from electrocardiographic orphotoplethysmographic signals, for example. MAD Mean absolute differenceof successive heartbeat intervals. Typically refers to a measured HRVlevel. MADmn or minMAD or Typically refers to the lowest HRV valueMADmin that has been measured during ongoing exercise. Heart ratesegment Accepted (=reliable) data points in AnT estimation may beclassified to selected narrow segments regarding heart rate coveringAnT. There may be one or more segments below and one or more segmentsabove expected AnT. For example, eight successive segments can be usedbetween 79-95% HRmax or 80-96% HRmax. Accordingly, the intensity rangeof each segment can be 2% HRmax, for example. Freely performed physicalAn exercise that may be performed without exercise a specific protocol.The user may freely decide the intensity of exercise, as well asrecovery periods inside the exercise session. Continuous measurementContinuous measurement of heart rate or external workload duringexercise may include any type of measurement that is done through thewhole exercise. Continuous measurement may also refer to measurementsthat are done intermittently throughout the exercise: All data can berecorded but it is also possible to record for example 1 minute of dataafter every 3 min of exercise (for example 0-1 min recording, 1-3 minnot recording, 3-4 min recording, 4-6 min not recording . . .) P1-P3 . .. Pi Probability factors that are used to calculate the totalprobability of AnT at each heart rate segment EPOC Excess post-exerciseoxygen consumption. As it can be nowadays estimated or predicted - basedon heart rate or other intensity derivable parameter - it can be used asan cumulative measure of training load in athletic training and physicalactivity. Critical power or critical A level of power output or velocitythat velocity or critical can be maintained for relatively long speedperiods. At critical power (velocity) exhaustion may occur after 20-40min of exercise. Critical power (or critical velocity) correlates withAnT.

BRIEF DESCRIPTION OF THE FIGURES

Advantages of embodiments of the present disclosure will be apparentfrom the following detailed description of the exemplary embodiments.The following detailed description should be considered in conjunctionwith the accompanying figures in which:

FIG. 1 presents an exemplary flowchart illustrating an implementation inan exemplary digital device.

FIG. 2 is a continuation of FIG. 1 and presents an exemplary flowchartillustrating the implementation in the exemplary digital device.

FIG. 3 presents an exemplary chart of interaction between cycling poweroutput, running speed and theoretical oxygen consumption.

FIG. 4 presents an exemplary chart showing heart rate, speed, altitudeand theoretical VO2 (calculated based on speed and altitude) collectedduring a normal training session.

FIG. 5 presents an exemplary chart showing measured heart rate (points)and a line of best fit (solid line) as a function of external work(theoretical VO2) based on the data shown in FIG. 4, and usual patternrespiration rate (dotted line) and heart rate variability (dashed line).

FIG. 6 presents an exemplary chart showing the effect of currentintensity (as a percentage of a session's highest speed) on P1 valuewhen current MAD value remains constant.

FIG. 7 presents an exemplary chart showing the effect of current MAD (aspercentage of session's minimum MAD) on P1 value when current speedvalue remains constant.

FIG. 8 presents an exemplary chart showing the effect of changing speedand heart rate on P2 value.

FIG. 9 presents an exemplary chart showing a changing relationshipbetween speed and heart rate, and their consequent effect on P2 value.

FIG. 10 presents an exemplary chart showing the effect of stored heartrate in respective segment (i) (% HRmax) on P3 value.

FIG. 11 presents an exemplary block diagram of a system with additionalinterfaces.

FIG. 12 presents an exemplary chart showing critical velocity andcritical power.

DETAILED DESCRIPTION

Aspects of the invention are disclosed in the following description andrelated drawings directed to specific embodiments of the invention.Alternate embodiments may be devised without departing from the spiritor the scope of the invention. Additionally, well-known elements ofexemplary embodiments of the invention will not be described in detailor will be omitted so as not to obscure the relevant details of theinvention. Further, to facilitate an understanding of the descriptiondiscussion of several terms used herein follows.

As used herein, the word “exemplary” means “serving as an example,instance or illustration.” The embodiments described herein are notlimiting, but rather are exemplary only. It should be understood thatthe described embodiments are not necessarily to be construed aspreferred or advantageous over other embodiments. Moreover, the terms“embodiments of the invention”, “embodiments” or “invention” do notrequire that all embodiments of the invention include the discussedfeature, advantage or mode of operation.

Further, many of the embodiments described herein are described in termsof sequences of actions to be performed by, for example, elements of acomputing device. It should be recognized by those skilled in the artthat the various sequences of actions described herein can be performedby specific circuits (e.g. application specific integrated circuits(ASICs)) and/or by program instructions executed by at least oneprocessor. Additionally, the sequence of actions described herein can beembodied entirely within any form of computer-readable storage mediumsuch that execution of the sequence of actions enables at least oneprocessor to perform the functionality described herein. Furthermore,the sequence of actions described herein can be embodied in acombination of hardware and software. Thus, the various aspects of thepresent invention may be embodied in a number of different forms, all ofwhich have been contemplated to be within the scope of the claimedsubject matter. In addition, for each of the embodiments describedherein, the corresponding form of any such embodiment may be describedherein as, for example, “a computer configured to” perform the describedaction.

The method can be implemented in versatile devices, which have resourcesfor measuring internal intensity and external workload, and run softwareto execute processes depicted in the exemplary flowcharts of FIGS. 1 and2. A schematic hardware assembly is depicted below in exemplary FIG. 11.

Initial background and personal data may be stored. For example, theperformance level (for example VO2max or METmax) and the maximum heartrate (HRmax), and the like, of the user may be stored. Personal data maybe entered or determined beforehand.

In one exemplary embodiment, AnT may be determined as shown in exemplaryFIGS. 1-2. At step (10) the digital device may continuously monitorheart rate (HR) and speed of a user. Time (t) may be monitoredinternally by, for example, a central processing unit (CPU) of thedevice. The raw data may be filtered initially using chosen thresholdvalues (12) for elapsed time, heart rate, heart rate variability orother value. The external work data may also be filtered using, forexample, one or more artifact criteria.

In step (14) heart rate variability (HRV) values, for example, MAD (meanabsolute difference), may be calculated and stored. The data point maybe filtered out at step (16) if, for example, the MAD value is too high,or speed is not stable.

Further exemplary embodiments may include easy filtering phases, acalculation of characterizing probability value P1 for a certainHR-segment, and a determination if more complex calculation is needed,whereby a full calculation may be executed only in a chosen situation.At step (20), when the calculation of P1 takes place, the segment (i)data corresponding to the actual measured heart rate may be determined.This data may contain values of the exact heart rate HR[i], probabilityP1, registered speed (v), registered time, and the like. At step (24),an index register is updated when new value P1 is better than acorresponding value P1[i] in the segment record (22) of a respectiveheart rate range. The segment records (22) may have the fields for bestdata point in each segment, for example, heart rate, calculatedprobability P1, speed (v), time, and the like.

At step (18), the probability P1 may be calculated, for example, usingvariables speed (v), maximum speed (v_(max)), mean average HRV (MAD),minimum MAD (MADmin), and the like.

At step (25), after index registers (20) are updated, a request of AnTmay exist, and if so, at step (26), probability factors, for example P2and P3, may be calculated. If AnT is not requested, the execution mayreturn to check a next data point.

In some exemplary embodiments, probability P2 may be calculated as anegative part in total probability based on, for example, how muchchange in external workload deviates from expected change in externalworkload, or other factors as would be understood by a person ofordinary skill in the art. Expected change (segment (i) and segment(i−1)) in external workload may be calculated based on change in heartrate between values in respective segments. Expected change in externalworkload may be calculated based on METmax. The change may be calculatedin values between a chosen segment (i) and any segment with lower index.

In further exemplary embodiments, probability P3 may be based on, forexample, expected value of 90% of maximum heart rate that is known as apopulation reference value for AnT, but could be based on alternativemeasurements as would be understood by a person of ordinary skill in theart. Because it is possible that fit individuals may have higher (andsedentary individuals lower) AnT than 90% of maximum heart rate, it isobvious that an adaptive reference value may be used instead of fixed90% value.

At step (28), total probability (P) for a recorded HR in each segment(i) may then be calculated through P1, P2 and P3.

In other words, P[i]=P(P1[i], P2[i], P3[i]).

Finally, at step (30) an estimate of anaerobic threshold (AnT) may becalculated as a weighted value from all AnT candidates in all segments(i) according to the following exemplary equation.

${{An}\;{T(t)}} = \frac{\sum\left( {{{HR}\lbrack i\rbrack}*\left( {P\lbrack i\rbrack}^{y} \right)} \right.}{\sum\left( {P\lbrack i\rbrack}^{y} \right)}$

The registered heart rate value and external workload (AnT-candidate) ofeach segment may be multiplied by the total probability of that segmentwhen all weighted AnT-candidates are added together.

In the example above, the probability values P2 [i] and P3 [i] may eachhave a matrix with eight elements (as also P1 may have). However, thecalculation may be modified such that only a limited number of registersis needed when, for example, steps (26), (28) and (30) are executedsimultaneously. In some exemplary embodiments, the components of theweight function could be calculated in the index order.

Exemplary FIGS. 6-10 may illustrate relationships of major components inthe probability factors P1, P2 and P3.

When calculating weight-values, the probabilities of the segments areemphasized by a chosen power ‘y’. In some exemplary embodiments, valuesof ‘y’ are 3-6, but could be any value as would be understood by aperson of ordinary skill in the art. The weight function (at step 30)may use high power (here ‘4’) for the probability P[i]. This may reducethe influence of lower probabilities. However, in a case where manysegments have similar probabilities, the result value may bettercorrespond to the true physiological AnT-value this way than by pickingthe data point with the highest probability.

In one exemplary embodiment a user (e.g. an athlete or keep fitenthusiast) may start an exercise session. The type of exercise can beeither interval or continuous. The user can freely decide the intensityof exercise, as well as recovery periods inside the exercise session.Heart beat interval data and performance data can be continuouslymeasured (speed and altitude or power output) during the exercise using,for example, a heart rate monitor, wristop computer or other relateddevice as would be understood by a person of ordinary skill in the art.Even a heartbeat sensor that is connected to a mobile phone or PDAdevice (using for example Bluetooth connection) can be used, in whichcase the mobile phone or PDA device would measure external workload(speed and altitude) and serve as a CPU unit. The system maycontinuously validate and calculate heart beat and performance data andmay form an estimate of a user's anaerobic threshold (AnT). Continuousmeasurement of HR during exercise may include any type of measurementthat is done through the whole exercise. For example, all RR-intervalsmeasured from electrocardiographic during exercise are usually recorded,but in the case of photoplethysmographic signal it is typical that beatto beat intervals are recorded only intermittently during exercise.However, despite intermittent recording, photoplethysmographic signalcan be also used.

In further exemplary embodiments the user may exercise outdoors. Theuser can exercise, for example, by walking or running. In someembodiments heart rate may be measured using a heart rate transmitterbelt, or the like, and analyzed in a CPU-unit that can be, for example,a normal sports watch, wristop computer, or similar device as would beunderstood by a person of ordinary skill in the art. Alternatively, itmay be possible to use ppg-signal processing so that both themeasurement and analysis of data may be done using a wristop device, orthe like. Measurement of speed and altitude can be done using a GPSsignal. The GPS receiver may be embedded, for example, in the wristopdevice, but an external GPS receiver can be used as would be understoodby a person of ordinary skill in the art. Altitude data can be retrievedfrom GPS data, additional barometer data, and the like. A barometer maybe embedded in the wristop computer. In the described exemplaryembodiments a user may, for example, walk or run (or both) during theexercise. The terrain can be whatever the user wants, for example, hillyor flat. During the exercise, data points may be continuously validatedand calculated. The AnT estimate can be shown to the user during theexercise, or after exercise, as desired.

In some of the above described exemplary embodiments, heart beat data,speed data and altitude data may be gathered and used, for example, whenthe user is exercising on foot (walking/pole walking or running)outdoors. In still further exemplary embodiments, a WIFI technique, forexample, may be used so that positioning can be determined indoors. Itmay also be possible to use an accelerometer signal (for example anaccelerometer positioned on a user's foot or the like) to definewalking/running speed indoors or outdoors, and that data can be usedtogether with barometer data. It is also possible that the exercise isdone using a treadmill, or the like. In that case, it is also possiblethat the speed data can be retrieved from an accelometry signal, or thelike. In one exemplary embodiment a user can input treadmill speed datato the CPU while the heart beat data is continuously measured.

In some of the above described exemplary embodiments, a user mayexercise on foot either by walking/Nordic walking, running, or the like.It is also possible to define AnT in other exercise modes, for examplecycling or rowing, wherein the power output can be easily measured andretrieved. As would be understood by a person of ordinary skill in theart, power output can be measured in cycling, for example, using a powermeter embedded in pedals or chains, and this power data can be shown tothe user in a wristop device, or the like. In one exemplary embodimentrelated to cycling—speed and altitude data may be replaced with poweroutput data measured from a bicycle. The user can do the bicyclingexercise indoors or outdoors, and on any desired terrain. In someexemplary embodiments, AnT estimation may only require that bothpedaling power and heart beat interval data are measured.

Referring generally to the exemplary embodiments, where power output ismeasured, (e.g. cycling or speed and altitude of walking or running aremeasured) it is possible to increase the accuracy of AnT estimate bymeasuring performance data. This is because heart beat data can bemeasured continuously as a function of performance data. In thiscomparison the relationship between performance data and heart rateshould be linear. Data points which seem to be “outlier” points—whencompared to the linear relationship—can be excluded from the AnTestimation.

An example of interaction between cycling power output and running speedmay be presented below with reference to FIG. 3. On the basis of thisrelationship it is understood by a person skilled in art that the sameor similar calculation can be used in cycling (or rowing etc.) andwalk/run exercises. The power is presented as watts per user's weight(kg). It will be understood that this is only an example of thisinteraction in cycling using well known calculation formulas. Therelationship may be similar in the case of indoor rowing, or othersports. There are some factors, for example, sport specific efficiency(economy) of movement which may cause small variations in thisrelationship between different sports. For example, the followingcalculation formulas can be used for theoretical VO2:Theoretical VO2 of running (ml/kg/min)=0.2*(speed m/min)+0.9*(speedm/min)*TAN (grade of incline)+3.5Theoretical VO2 of walking (ml/kg/min)=1.78*(speed m/s)*60*(TAN(grade ofincline)+0.073)

A threshold speed of e.g. 7.5 km/h can be used in switching from walkingformula to running formula. Alternatively, detection between walking andrunning can be used using accelerometer data.

In cycling, power output can be converted to VO2 using the followingexemplary formula:Theoretical VO2 of cycling (ml/kg/min)=((power watts)*12+300))/weight

FIG. 4 presents examples of heart rate, speed, altitude and theoreticalVO2 (calculated based on speed and altitude) which may be collectedduring a normal training session.

FIG. 5 shows exemplary exercise (for example, running/walking) datarepresenting heart rate, speed and altitude recorded during exercise asa function of exercise time. In addition, theoretical oxygen consumption(VO2), that may be calculated based on speed and altitude, is furthershown in exemplary FIG. 5. Data presented in FIG. 5 could be derivedfrom the same exercise that has been shown in FIG. 4. In the examples ofFIG. 4 and FIG. 5, when this exercise data is observed in anotherperspective, for example as a function of theoretical VO2, the idea ofcalculation may be shown. In other words, near anaerobic threshold heartrate values often tend to show a downturn below linear dependency, heartrate variability is usually close to its minimum levels and heart ratederived respiration may start an exponential increase. Thesephysiological markers are searched from the data.

Still referring to FIG. 5, the exemplary measured heart rate (points)and the line of best fit (solid line) may be shown as a function ofexternal work (theoretical VO2). The dashed line may represent the heartrate variability index. As can be seen, the probability for AnT may behigh, for example, when HRV index is close to its minimum value and whenheart rate (points) starts a downturn compared with the lineardependency line (line of best fit). In addition, heart rate derivedrespiration rate can be used in AnT detection. Respiration rate (dottedline) may start to increase exponentially after AnT has been reached. Inthis example, respiration rate and heart rate variability lines aremerely schematic examples of the phenomena.

In some exemplary embodiments, the data (for example, HR, external work,HRV, HRV-based respiration parameter) may be compartmentalized, forexample, into 8 different 2% segments based on individual % HRmax. Moregenerally there may be anywhere from 5-30 segments and the size of eachsegment may be anywhere from 0.5-3%. The lower end in said range 5-30may suffer from a low resolution and the upper end may lead to a complexcalculation. Thus, a narrower range may be 6-15 segments.

The system may seek to find the most probable location of AnT using thedata from these segments that can locate, for example, withinapproximately 79-96% of individual HRmax (at least 7% difference betweenthe low end and the high end may be required, e.g. 89-96% of HRmax).There can be similar kinds of restrictions for HRV and external work, aswould be understood by a person of ordinary skill in the art.

Within each segment, a suitable data point to represent that specificsegment may be chosen from all data candidates according to, forexample, quality metrics of the data, fuzzy logics and without usingthresholds.

The possibility for a suitable data point to be chosen may increase, forexample, when heart rate variability is at a low level, and may behighest when HRV is the lowest possible within segment.

The possibility for a suitable data point to be chosen may increase, forexample, when external workload (e.g. speed or theoretical VO2 or poweroutput) is high, and when external workload is stable.

The data points may be evenly distributed over the exercise.

The system may assume that the data for lower external work are takenfrom an early part of the exercise, if available, and the data forhigher external work are taken from the later part of the exercise, ifavailable.

Probabilities (P1-P3) of each of the chosen data points to be theAnT-estimate may be calculated. For example, in the case of P2calculation, heart rate from the chosen data points may be compared toexternal work, and linear increase may be expected for heart rate whenexternal workload increases (as shown in FIG. 5). As an example of P2,outlier points may have lower probability-value. Above AnT intensity, adownturn of heart rate may occur causing these measurement points todeviate from a linear regression line, thus possibly leading to lower P2values.

If the data point representing a high intensity segment is timelylocated significantly later than any or some other chosen data points,and does not show significant decrease in the heart rate vs. speedrelationship, the probability of that point or higher intensity datapoint to be the AnT may be increased (P2-probability increases).

In some exemplary embodiments, approximately 90% of HRmax may be used asa population level reference, and the probability of the data point tobe defined as AnT may be increased when locating closer to thatreference (P3-probability increases).

In still other exemplary embodiments, a heart rate variability (HRV)measure may be used in the AnT determination. Heart rate variability maybe high at low intensities and may start to decrease when exerciseintensity rises to a moderate level, for example, over 60% of maximalheart rate. Between about 60-80% heart rate variability may beconsidered a low level. The point at which variability starts toincrease again, for example, between 85 and 90%, may be close to AnT.This is because the acidity induced sudden increase in ventilation maycause increased variability in heart rate (differences in successiveheart beat intervals). Data points located close to “HRV increase” mayhave a high AnT probability (P1-probability increases).

Attributes of the data points (HR, external work, P1, t, and the like)may be stored.

By using probabilities calculated for each segment, AnT may becalculated as, for example, a weighted sum from all segments.

AnT can be provided for the user or other system in real-time or afterthe exercise, as desired.

If data points do not appear to include AnT, extrapolation of the datacan be used to specify the most probable AnT location as % HRmax or HRlevel. Extrapolation can be made based on the slope-value of heart rateas a function of external workload in the highest segment with storedheart rate. In the case that slope value is high when exercise's highestmeasured heart rate is reached, say at 87% HRmax, it may be concludedthat AnT locates at 92%, for example. On the other hand, the sameexample in perspective, if the slope-value would have been low it couldbe concluded that AnT locates at 87%. Slope-value expresses therelationship between change in heart rate and external workloadcalculated from values in respective segments. The extrapolation ofAnT—value is based on empiric model or function. Another embodiment mayrequire for giving AnT estimate that a user reaches a certain heart ratelimit during exercise that may be 90% of maximum heart rate, forexample.

The accuracy of the method may increase when more data is collected fora specific user by learning from the user data.

The method and the system can be used in any type of exercise where HRand external work can be measured, as would be understood by a person ofordinary skill in the art.

Further exemplary embodiments for the calculation of previouslydescribed probabilities are described below. In one exemplaryembodiment, three probability factors P1-P3 may be used, although moreprobability factors may be used as desired. AnT may be determined as aweighted average of these probabilities.

In some exemplary embodiments, the mathematical formula for P1 may beexpressed as Matlab®M-code:P1=(1500−100*(vmax−v)−600*(MAD−MADmin))/15,wherein v=current speed (km/h; watts from e.g. cycling can betransformed to km/h scale if needed according to FIG. 3), vmax=highestmeasured speed, MAD=current HRV-level, MADmin=lowest measured HRV-level.

If a current P1 value is lower than a P1 value already measured for thatHR segment [i], then the P1 current value may be rejected. Otherwise,the current P1 value may be recorded as one AnT candidate (HR, v, P1,t), where t is a current time moment.

The idea of this P1 calculation is that a higher current speed (relativeto highest speed measured during exercise) may result in a higher P1value, because increasing or steady state intensities may be morereliable for determining AnT as compared with decreasing intensity. FIG.6 shows an example of the effect that current speed (as a percentage ofsession's highest speed) may have on P1 value when current MAD value isconstant.

In addition, AnT probably occurs in intensities where heart ratevariability (=MAD) is close to a minimum value. P1 may decrease when thedifference between current MAD and minimum MAD increases. FIG. 7 showsan example of the effect that current MAD (as a percentage of session'sminimum MAD) may have on P1 value when current speed value is constant.

In further exemplary embodiments, the mathematical formula for P2 may beexpressed as Matlab®M-code:

P2(i)=mean(((v(i)−v(1:i−1))/((phr(i)−phr(1:i−1))*maximal_MET*1.5625)−1)/max(1,abs(t(i)−t(1:i−1))/5)),wherein v (i) is a user's stored speed in respective segment (or speedcalculated from watts), phr (i) is a user's stored heart rate relativeto a maximal heart rate in respective segment and maximal MET is auser's maximal exercise capacity. In the above formula for P2 the part“v(i)−v(1:i−1)” may calculate the actual change in speed and“(phr(i)−phr(1:i−1))/100*maximal_MET*1.5625)” may describe how much auser's speed should increase relative to the increase in measured heartrate. As would be understood by a person of ordinary skill in the art, auser's maximal exercise capacity may influence this relationship. Inaddition, the gap in time between two moments may be taken into accountto decrease its influence on P2 when the gap is increasing above fiveminutes, for example: M-code“max(1,abs(t(i)−t(1:i−1))/5)”

The calculation of P2 may be used to decrease the AnT-probability of ameasurement point, where heart rate may not stay in line with externalworkload (theoretical oxygen consumption, watts or speed). FIG. 8 showsan example of the effect that changing speed and heart rate may have onP2 value. In the example of FIG. 8, heart rate has increased 6% and theconsequent change in speed may determine the P2 value. The value mayonly be taken into account if it is below 0. As would be understood by aperson of ordinary skill in the art, in classic probability calculus,P-values cannot reach values below 0, but in fuzzy logics they may. FIG.8 may represent just one exemplary case, and a broader example may bepresented in FIG. 9 where more cases (heart rate increases between 6%and 10% and speed between −0.7 and +0.7 km/h) have been represented.Positive values have been excluded from exemplary FIG. 9. A person'smaximum performance capacity (METmax or maxMET in this disclosure) maybe one influencing factor. METmax value depends on personalphysiological characteristics and training history. METmax typicallyvaries between about 10-15 METs. The extent to which P2 value changes(due to heart rate vs. external workload relationship) may depend on aperson's METmax-value. METmax value itself may be determined during theearly phases of exercise as is disclosed in applicants's other patentapplications (WO2015036651 and WO2012140322). Other options fordetermining METmax may comprise use of current and history exercisestogether, or METmax value may be set by the user before an exercise as abackground parameter. In FIG. 8 and FIG. 9 an arbitrary value has beenused for METmax and graphs may vary if lower or higher METmax value hadbeen used.

In further exemplary embodiments, calculation of the probability P3 maybe expressed as Matlab®M-code:P3(i)=100*(101−phr(i))/11, when phr(i)>90%P3(i)=100*(11+phr(i)−90))/11, when phr(i)<=90%,

-   -   wherein phr(i) is a user's stored heart rate in segment (i)        proportional to the maximum heart rate of the user.

The calculation of P3 may be desired in that, according to empiricalevidence AnT may occur at approximately 90% of maximal heart rate.Accordingly, the probability may decrease when intensity moves away fromthis intensity. FIG. 10 may show an example of the effect that storedheart rate in segment (i) (% HRmax) may have on P3 value.

In one exemplary embodiment, other signals—in addition to heart rate andexternal workload—may be measured and accompanied into the AnTestimation. One such signal is, for example, electromyographic (EMG)signal from muscles. EMG signal magnitude may provide one additionalmeasure that could be taken into account in AnT estimation since therehas been found AnT—induced non-linear change in EMG signal magnitude inincremental exercise tests. This threshold-like change may be associatedto change in motor unit recruitment pattern when fast (type 2) musclefibers are needed to be able to increase external workload.EMG-measuring pants are an example of a meaningful state-of-art methodthat could be incorporated to the exemplary embodiments in thisdocument. Measured EMG data could be transmitted to the CPU by usingBluetooth, for example.

State-of-art measurement methods may allow for integration of(transthoracic) bioimpedance recording equipment into a heart ratetransmitter belt. In that case measurement of transthoracic impedancemay allow a more detailed analysis of user's respiration rate and tidalvolume, and consequently ventilation. These respiratory measures mayincrease the accuracy of AnT estimation when incorporated into theexemplary embodiments disclosed in this application.

It has been disclosed in this document that accuracy of the method mayincrease when more data is collected for a specific user by learningfrom the user data. When more measurement data is “fed” to the system,it is possible to use for example weighted average as a result toincrease the robustness of the method. So if AnT-estimate heart rate ona previous day has been, for example 180, and on the day after it seemsto be 174, then average value of 177 can be displayed to the user. Onepossible embodiment may also be to allow a user to set a measured AnTvalue as background parameter. Then this value could be used as onereference point in addition to actual estimates. It is also possible toweight AnT—estimates above regarding calculation of weighted average.More weight may be given for a reliable estimate: For example if thereis a wide range of stored data regarding heart rate and number ofsegments. AnT estimates may be taken from different exercises ondifferent days.

There are also other ways to teach the system about a user's physiology:it may be possible to scan user data to find, for example, record timesfor different distances. Average speed (or theoretical VO2) can beobserved in the case of running and average watts (per user's weight) inthe case of cycling. Examples of this relationship may be shown in FIG.12. Speed value and watt/kg value—where the line starts to level of—iscalled critical velocity or critical power. As mentioned earliercritical velocity/power correlates with AnT. By estimating criticalvelocity or critical power from history data it may be possible toincrease accuracy of the AnT—estimate.

AnT may play an important role in training load and training effectassessment because training load may increase dramatically afterintensity exceeds a person's AnT. Accordingly, more accurate AnTestimation as described in this disclosure can be utilized in many ways.In one exemplary embodiment, AnT may be used in calculation of trainingload (e.g. EPOC) and training effect during or after exercise.

In further exemplary embodiments, AnT may be utilized in trainingprescription. For example, a user can have a goal to improve marathontime. In that case, the user may desire to improve running speedcorresponding to anaerobic threshold (v_ant), because a marathon raceneeds to be run without continuous lactate accumulation. As would beunderstood by a person of ordinary skill in the art, a basic rule ofspecificity of training (related to improvement of physiological orbiomechanical characteristics) states that the intensity-range (speedrange) most commonly used in training also improves the most. Forexample, marathon runners try to train as much as possible near AnTintensities, thereby trying to improve v_ant. Improvement of v_ant maybe induced by, for example, improved running economy (lower oxygen costof running), improved lactate removal, improved aerobic metabolism(improved fat metabolism), and the like. Due to more accurate AnTestimates, training prescription can be focused more accurately on AnTintensities, and consequently, v_ant can be improved more efficiently.

In some exemplary embodiments, a user may have a goal to improveperformance in, for example, a 3 km or 5 km run, in a cooper (12 min)running test, or the like. In these embodiments, training of maximaloxygen uptake (VO2max) may be highly important. Because exerciseintensities just above anaerobic threshold are very efficient inimproving VO2max, it should be understood by a person of ordinary skillin art that the described embodiments can be efficiently utilized in aVO2max training prescription.

In still further exemplary embodiments the determined AnT may beutilized in selecting a target pace for a running or cycling race. Forexample, after a pace corresponding to AnT has been determined for aperson, for example a target pace of 90% of that pace can be set for amarathon. Still further, the marathon target speed could be adjustedbased on the fitness level of the user. For example, a target pace of95% of v_ant may be set for a person with very high fitness level. Thesystem and method according to the exemplary embodiments can be appliedin many kinds of devices as would be understood by a person of ordinaryskill in the art. For example, a wrist top device with a heart-ratetransmitter, a mobile device such as a phone, tablet or the like, orother system having CPU, memory and software therein may be used.

According to exemplary FIG. 11, in the implementation may include anassembly built around a central processing unit (CPU) 32. A bus 36 maytransmit data between the central unit 32 and the other units. The inputunit 31, ROM memory 31.1, RAM memory 31.2, keypad 18, PC connection 37,and output unit 34 may be connected to the bus.

The system may include a data logger which can be connected to cloudservice, or other storage as would be understood by a person of ordinaryskill in the art. The data logger may measure, for example,physiological response and/or external workload.

A heart rate sensor 42 and any sensor 40 registering external workloadmay be connected to the input unit 31, which may handle the sensor'sdata traffic to the bus 36. In some exemplary embodiments, the PC may beconnected to a PC connection 37. The output device, for example adisplay 45 or the like, may be connected to output unit 34. In someembodiments, voice feedback may be created with the aid of, for example,a voice synthesizer and a loudspeaker 35, instead of, or in addition tothe feedback on the display.

The sensor 40 which may measure external workload may include any numberof sensors, which may be used together to define the external work doneby the user.

More specifically the system presented in FIG. 11 may have the followingparts for determining a body's readiness to respond to physical exerciseand provide feedback to a user:

-   -   a heart rate sensor 42 configured to measure the heartbeat of        the person, the heart rate signal being representative of the        heartbeat of the user;    -   at least one sensor (40) to measure an external workload during        an exercise, and    -   a data processing unit (32) operably coupled to the said sensors        (42, 40), a memory (31.1, 31.2) operably coupled to the data        processing unit (32),    -   the memory may be configured to save background information of a        user, for example, background data including an earlier        performance level, user characteristics, and the like.

The data processing unit (32) may include dedicated software configuredto execute the embodiments described in the present disclosure.

As described above in the exemplary embodiments, default values of theoptional parameters (for example, P2 and P3) may be stored in a ROMmemory, in an EEPROM (Electrically Erasable Programmable Read-OnlyMemory) memory, or in other memory as would be understood by a person ofordinary skill in the art.

The foregoing description and accompanying figures illustrate theprinciples, preferred embodiments and modes of operation of theinvention. However, the invention should not be construed as beinglimited to the particular embodiments discussed above. Additionalvariations of the embodiments discussed above will be appreciated bythose skilled in the art.

Therefore, the above-described embodiments should be regarded asillustrative rather than restrictive. Accordingly, it should beappreciated that variations to those embodiments can be made by thoseskilled in the art without departing from the scope of the invention asdefined by the following claims.

The invention claimed is:
 1. A method for determining anaerobicthreshold intensity (AnT) of a user in a freely performed physicalexercise, comprising: continuously measuring, by a heart rate sensor, aphysiological response of a user by heart rate, wherein a plurality ofmeasured heart rate values are recorded with time stamps as heart ratedata; continuously measuring an external workload including at least oneof speed and velocity, wherein a plurality of measured workload valuesare recorded and each measured workload value is associated with one ofthe plurality of measured heart rate values to form a plurality of datapoints; filtering, by a processor, one or more data points based on apredetermined criteria to form a plurality of accepted data points;classifying, by the processor, accepted data points within a pluralityof heart rate segments representing a heart rate within an anaerobicthreshold (AnT) of the user, wherein at least one segment is below aprobable heart rate value of AnT, and at least one segment is above theprobable heart rate value of AnT; calculating, by the processor, a firstprobability factor for each of the plurality of accepted data pointsbased on the measured external workload value and a measured heart ratevariability of each data point; comparing, by the processor, thecalculated first probability factor with at least one probability factorin a respective segment, and if the first probability factor is higherthan the at least one stored probability factor, the first probabilityfactor replaces the at least one stored probability factor in therespective segment so that the data point with the highest firstprobability factor is stored in a memory for each segment; calculating,by the processor, an estimate of the user's AnT as a weighted value ofheart rate values recorded for each segment, wherein each recorded heartrate value in each segment is multiplied by the first probability factorstored for that segment when all weighted values are added together;outputting, by the processor to a display, the estimate of the user'sAnT.
 2. The method of claim 1, wherein the first probability factor iscalculated based on the relationship between current measured externalworkload and highest measured external workload, and the relationshipbetween current measured heart rate variability level and lowestmeasured heart rate variability level.
 3. The method of claim 2, whereinthe first probability factor (P1) is calculated according to theequation:P1=(1500−100*(vmax−v)−600*(MAD−MADmin))/15, wherein v=current measuredexternal workload as at least one of current speed or velocity,vmax=highest measured external workload as at least one of highest speedor velocity, MAD=current measured heart rate variability level, andMADmin=lowest measured heart rate variability level.
 4. The method ofclaim 1, wherein the estimate of the user's AnT as a weighted value ofthe recorded heart rates (HR) of each segment (i) is calculatedaccording to the equation:${{An}\;{T(t)}} = \frac{\sum\left( {{{HR}\lbrack i\rbrack}*\left( {P\lbrack i\rbrack}^{y} \right)} \right.}{\sum\left( {P\lbrack i\rbrack}^{y} \right)}$wherein P[i] is the first probability factor stored for each segment(i), and y is a chosen power value.
 5. The method of claim 1, whereinthe estimate of the user's AnT can be displayed to the user during theexercise or after the exercise.
 6. The method of claim 1, wherein theestimate of the user's AnT can be displayed to the user as a valuedescribing external workload.
 7. The method of claim 1, wherein theplurality of heart rate segments include 5-30 segments.
 8. The method ofclaim 7, wherein the size of each segment is 0.5-3% of the user'smaximum heart rate.
 9. A system for determining anaerobic thresholdintensity (AnT) of a user in a freely performed physical exercise,comprising: means for continuously measuring a physiological response ofa user by heart rate, wherein a plurality of measured heart rate valuesare recorded with time stamps as heart rate data; means for continuouslymeasuring an external workload including at least one of speed andvelocity, wherein a plurality of measured workload values are recordedand each measured workload value is associated with one of the pluralityof measured heart rate values to form a plurality of data points; meansfor filtering one or more data points based on a predetermined criteriato form a plurality of accepted data points; means for classifyingaccepted data points within a plurality of heart rate segmentsrepresenting a heart rate within an anaerobic threshold (AnT) of theuser, wherein at least one segment is below a probable heart rate valueof AnT, and at least one segment is above the probable heart rate valueof AnT; means for calculating a first probability factor for each of theplurality of accepted data points based on the measured externalworkload value and a measured heart rate variability of each data point;means for comparing the calculated first probability factor with atleast one stored probability factor in a respective segment, and if thefirst probability factor is higher than the at least one storedprobability factor, the first probability factor replaces the at leastone stored probability factor in the respective segment so that the datapoint with the highest first probability factor is stored for eachsegment; means for calculating an estimate of the user's AnT as aweighted value of heart rate values recorded for each segment, whereineach recorded heart rate value in each segment is multiplied by thefirst probability factor stored for that segment when all weightedvalues are added together; and means for outputting and displaying theestimate of the user's AnT.
 10. The system of claim 9, wherein the firstprobability factor is calculated based on the relationship betweencurrent measured external workload and highest measured externalworkload, and the relationship between current measured heart ratevariability and lowest measured heart rate variability level.
 11. Thesystem of claim 9, wherein the estimate of the user's AnT can bedisplayed to the user during the exercise or after the exercise.
 12. Thesystem of claim 9, wherein the plurality of heart rate segments includebetween 5-30 consecutive 0.5-3% segments of the maximum heart rate ofthe user.
 13. A method for determining anaerobic threshold intensity(AnT) of a user in a freely performed physical exercise, comprising:continuously measuring, by a heart rate sensor, a physiological responseof a user by heart rate, wherein a plurality of measured heart ratevalues are recorded with time stamps as heart rate data; continuouslymeasuring an external workload including at least one of speed andvelocity, wherein a plurality of measured workload values are recordedand each measured workload value is associated with one of the pluralityof measured heart rate values to form a plurality of data points;filtering, by a processor, one or more data points based on apredetermined criteria to form a plurality of accepted data points;classifying, by the processor, accepted data points within a pluralityof heart rate segments representing a heart rate within an anaerobicthreshold (AnT) of the user, wherein at least one segment is below aprobable heart rate value of AnT, and at least one segment is above theprobable heart rate value of AnT; calculating, by the processor, a firstprobability factor for each of the plurality of accepted data pointsbased on at least the measured external workload value and a measuredheart rate variability of each data point; comparing, by the processor,the calculated first probability factor with at least one storedprobability factor in a respective segment, and if the first probabilityfactor is higher than the at least one stored probability factor, thefirst probability factor replaces the at least one stored probabilityfactor in the respective segment so that the data point with the highestfirst probability factor is stored in a memory for each segment;calculating, by the processor, AnT using the stored probabilities ineach segment; and outputting, by the processor to a display, theestimate of the user's AnT.
 14. The method of claim 13, wherein thefirst probability factor is calculated based on the relationship betweencurrent external workload and highest measured external workload, andthe relationship between current and lowest measured heart ratevariability level.
 15. The method of claim 13, wherein the firstprobability factor (P1) is calculated according to the equation:P1=(1500−100*(vmax−v)−600*(MAD−MADmin))/15, wherein v=current externalworkload as at least one of current speed or velocity, vmax=highestmeasured external workload as at least one of highest speed or velocity,MAD=current heart rate variability level, and MADmin=lowest measuredheart rate variability level.
 16. The method of claim 15, wherein theestimate of the user's AnT as a weighted value of the highest recordedheart rates (HR) of each segment (i) is calculated according to theequation:${{An}\;{T(t)}} = \frac{\sum\left( {{{HR}\lbrack i\rbrack}*\left( {P\lbrack i\rbrack}^{y} \right)} \right.}{\sum\left( {P\lbrack i\rbrack}^{y} \right)}$wherein P[i] is the first probability factor stored for each segment (i)and y is a chosen power value.
 17. The method of claim 13, wherein theplurality of heart rate segments includes 5-30 segments.
 18. The methodof claim 13, wherein the plurality of heart rate segments includes 6-15segments.
 19. The method of claim 18, wherein a size of each segment is0.5-3% of the user's maximum heart rate.
 20. The method of claim 13,wherein a final AnT estimate is calculated from at least two AnTestimates weighted by a chosen criteria.
 21. The method of claim 13,wherein an AnT estimate is given to a user only if exercise heart ratereaches a chosen limit.